Does the warm-up effect subsequent post activation performance enhancement?
Bibliographic record
Abstract
The purpose of the following study was first to identify an optimal warm-up to maximise countermovement jump (CMJ) performance, and second to investigate whether a conditioning activity (CA) of half-squats could potentiate CMJ performance above that of the optimal warm-up.Sixteen resistance trained males were recruited for the study.Participants performed six different warm-up volumes over six sessions.Warm-ups consisted of submaximal running, dynamic stretching and practice CMJs.After the warmup, participants rested for four minutes before performing three CMJs on a force platform.The warm-up which resulted in the best CMJ relative peak power (RPP) was considered to be that individual's optimal warm-up.Participants attended another testing session where they performed their optimum warm-up followed by a pre-CMJ test.Participants then performed a CA of four half-squats with a 5RM load followed by post-CMJ tests after fourand eight-minutes recovery.No CMJ variable displayed significant improvements at either four or eight minutes recovery after the CA when compared to the pre-test.However, when everyone's optimum recovery period was considered, CMJ height significantly improved by 5.2% (p = 0.009) when compared with pre-CMJ performance.If the optimum recovery period is considered, a half-squat CA can further improve CMJ height above that of a general warm-up alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".